#LangChain

Question count - 0     Last updated    - V4 (19-Nov-2024)

LangChain	LangChain is a framework designed to develop applications powered by large language models (LLMs). It simplifies the process of building complex AI systems by connecting LLMs to external data and other computational resources, overcoming limitations of LLMs working solely on input prompts.
Core components	Document Loaders: Easily ingest data from various sources (PDFs, websites, databases, etc.) making it accessible to the LLM. Text Splitters: Break down large documents into smaller, manageable chunks suitable for LLM processing, respecting context window limitations. Embeddings: Convert text into numerical representations (vectors) that capture semantic meaning, crucial for similarity search. Vectorstores: Efficiently store and search vector embeddings, enabling quick retrieval of relevant information. Retrievers: Fetch the most pertinent information from the vectorstore based on user queries, supplying context to the LLM. LLMs: LangChain supports multiple LLMs, providing flexibility to choose the best model for specific tasks. Prompt Templates: Create and manage prompts effectively, enabling dynamic input and output formatting for consistent LLM responses.
Why use LangChain framework?	Simplified Development: Abstracts away low-level complexities, allowing developers to focus on application logic. Data Integration: Seamlessly connects LLMs with external data sources, enhancing capabilities and accuracy. Enhanced Functionality: Enables building applications that go beyond simple question-answering. Improved Context Management: Handles context window limitations of LLMs by efficient data management. Model Agnosticism: Supports multiple LLMs, providing flexibility and adaptability.
How to get started with LangChain?	LangChain offers comprehensive documentation and tutorials to help you begin building your LLM applications. It's an open-source project with a vibrant community, providing ample support and resources.

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